Heuristic algorithms are often used to find solutions to real complex world problems. These algorithms can provide solutions close to the global optimum at an acceptable time for optimization problems. Social Spider Algorithm (SSA) is one of the newly proposed heuristic algorithms and based on the behavior of the spider. Firstly it has been proposed to solve the continuous optimization problems. In this paper, SSA is rearranged to solve discrete optimization problems. Discrete Social Spider Algorithm (DSSA) is developed by adding explorer spiders and novice spiders in discrete search space. Thus, DSSA's exploration and exploitation capabilities are increased. The performance of the proposed DSSA is investigated on traveling salesman benchmark problems. The Traveling Salesman Problem (TSP) is one of the standard test problems used in the performance analysis of discrete optimization algorithms. DSSA has been tested on a low, middle, and large-scale thirty-eight TSP benchmark datasets. Also, DSSA is compared to eighteen well-known algorithms in the literature. Experimental results show that the performance of proposed DSSA is especially good for low and middle-scale TSP datasets. DSSA can be used as an alternative discrete algorithm for discrete optimization tasks.

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解决旅行商问题的离散社会蜘蛛算法

启发式算法通常用于寻找现实复杂世界问题的解决方案。这些算法可以在可接受的时间为优化问题提供接近全局最优的解决方案。社交蜘蛛算法（SSA）是新提出的启发式算法之一，基于蜘蛛的行为。首先提出解决连续优化问题。在本文中，重新排列 SSA 以解决离散优化问题。Discrete Social Spider Algorithm (DSSA) 是通过在离散搜索空间中添加探索者蜘蛛和新手蜘蛛而开发的。因此，DSSA 的探索和开发能力得到了提高。在旅行商基准问题上研究了所提出的 DSSA 的性能。旅行商问题 (TSP) 是离散优化算法性能分析中使用的标准测试问题之一。DSSA 已经在低、中和大规模的 38 个 TSP 基准数据集上进行了测试。此外，将 DSSA 与文献中的 18 种著名算法进行了比较。实验结果表明，所提出的 DSSA 的性能尤其适用于中低规模的 TSP 数据集。DSSA 可用作离散优化任务的替代离散算法。实验结果表明，所提出的 DSSA 的性能尤其适用于中低规模的 TSP 数据集。DSSA 可用作离散优化任务的替代离散算法。实验结果表明，所提出的 DSSA 的性能尤其适用于中低规模的 TSP 数据集。DSSA 可用作离散优化任务的替代离散算法。